An ant colony hyperheuristic approach for matrix bandwidth reduction

Abstract

This paper considers the bandwidth reduction problem for large-scale matrices in serial computations. A heuristic for bandwidth reduction reorders the rows and columns of a given sparse matrix so that the method places entries with a nonzero value as close to the main diagonal as possible. Bandwidth optimization is a critical issue for many scientific and engineering applications. In this regard, this paper proposes an ant colony hyperheuristic approach for the bandwidth reduction of symmetric and nonsymmetric matrices. The ant colony hyperheuristic approach evolves and selects graph theory bandwidth reduction algorithms for application areas. This paper evaluates the resulting heuristics for bandwidth reduction in each application area against the most promising low-cost heuristics for bandwidth reduction. This paper also includes a numerical examination of the current state-of-the-art metaheuristic algorithms for matrix bandwidth reduction. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed approach outperformed state-of-the-art low-cost heuristics for bandwidth reduction when applied to problem cases arising from several application areas, clearly indicating the promise of the proposal.